4.1 Green Spaces and Prices
Although the cost of green building and construction is high as compared to simple one but these two factors move residential prices in unpredictable ways; green spaces (e.g. greenbelts, parks, green spaces etc.) can be expected, ceteris paribus, to shift the demand curve for residential housing to the right. An increase in green residential construction, ceteris paribus, can be expected to shift the supply curve for housing to the right. The net effect on prices is indeterminate which further fuel increase in value of residential properties. In Table (4) indicates that provision of more spaces for greenbelts and parks out of overall allocated land cause high cost as one can observe that in Bahria, airport housing society and Gulbarg green society cost/ value of per square feet is higher as compared to those societies who allocated less areas and constructed few units of green spaces in residential properties.
4.2 Community Centers and Price
Spaces for recreational activities and amenities for adults, kids, as well as families in residential areas include zoo, cricket grounds, basketball courts, tennis courts, golf course, gyms, mosques, and other community centers for entrainment and recreation purpose play a significant role in constructing a positive and prestigious identity before the customers (Owusu-Edusei, 2003; Rauf & Weber, 2020; Schläpfer et al., 2015). Although allotment for these activities cause high cost as shown in table (5) but they can play a vital role in constructing a positive identity before the customers. Urban open spaces are highly valued for their contribution to the quality of life in cities (Syed Ayub Qutub et al., 2015) but due to high population density in Pakistan, these spaces and unites are very limited and are not adequate for all. According to Numbeo.com data 2021, Islamabad secured 139.35 points in quality of life index which is at moderate level and Rawalpindi secured 81.40 that is very low (Nubmeo.com, 2021).
4.3 Environmental Dynamics and Prices
Urban pollution is very prominent environmental problem being faced by the citizen of Pakistan as Lahore is categorized as second populated city around the globe. Similarly, pollution index is reported at 41.71 points and 76.96 points in Islamabad and Rawalpindi, respectively (Numbeo, 2021). Islamabad owes its high air pollution to continuous mega development works such as road widening projects, increasing numbers of cars and particularly steel mills, a dominant source of air pollution (Dawn, 2018). Similarly, Rawalpindi has becoming highly populated city of Punjab after Lahore because there has no air quality monitors to gauge pollution in the garrison city of over 1.8 million populations with increasing number of automobiles, urbanization and settlements spoiling the ecology (The Nation, 2019). As shown in table (6) higher the pollution, lower the price in select sample housing societies.
4.4 Security and Safety Dynamics and Prices
We test the theory that a low crime rate will tend to push up the residential prices because the demand by buyers is higher in a low crime rate region that it will be in a high crime rate region. In this study, the crime rate is assigned a value of 1 if a property in the sample is in a high crime rate area and 0 if it is in a low crime rate area. A high crime rate area is defined as one where the number of reported crimes in that specific area is higher than the average. The statistical evidence displayed in table (7) shows that the average of prices of properties in high crime rate areas is significantly lower than the average of prices in low crime rate areas.
4.5 Housing Society Repute and Value of Real Estate Property
In order to test the role of prestige explaining the value of the real estate properties, we construct a binary-valued dummy variable: a high reputation is assigned a value of 1, 0 otherwise. We calculate the reputation of the housing societies based on pollution score, greenness score, community and commercial spaces score. Further to validate our calculation of dummy based on the socio-economic and environmental preferences, we apply Artificial Intelligence (AI) using Python. We find that greenness scores, commercial and social amenities play a vital role in defining the prestige of housing societies as shown in figure (1).
The result of AI model in table (8) indicates more than 85% accuracy and corrected deploying Vector Machine, Logistic Regression, Decision Tree, Random Forest and Artificial Neural Networks.
Similarly, on the basis of repute of housing society we calculate the value price. Table (09) displays statistical evidence showing that the prices of residential properties located in a high reputation housing authority are significantly larger than the prices of properties located in low reputation housing authority. This finding supports the signaling hypothesis that a higher prestige of societies can be construed as a signal that trading volume and liquidity of that authority are higher than low prestige. This accuracy is a manifestation of the transparency of the recording procedure of that specific authority and these societies are focusing on quality of life standard.
4.6 Determinants of Prices of the Local Housing Society
In order to test different characteristics of housing properties, we use overall hedonic model applying OLS to find out the prominent features that explain the pricing dynamics. In table (10), we find that size of house, no of bedroom, presence of garden, proximity to market, airport and greenbelt and crime rate ( \(\beta\)= 2.253, 0.470, 2.786, -0.559, -0.066, 0.072, and − 0.648, p < 0.05), respectively have significant role in defining the prices. Structure and architecture include, size, spaces, and facilities play a positive role and proximity to amenities and commercial centers negatively associated with pricing dynamics of real estate properties.
In Model 1–5, we added a pollution score, greenness score, community and commercial activities and prestige of housing authorities. The test statistics are AIC = 7690.11, BIC = 7633.20, and R2 = 0.63 when we include the pollution level in overall model. The value of \(\beta = -0.123\)or this variable is significant at the 99% confidence interval (p < 0.01) as shown in table (10). The explanatory power of this model is superior to that of Model-hedonic, as signified by a lower AIC and BIC and higher R2. This shows that pollution level in any residential areas do matter explaining the prices variation. Higher the level of pollution, more the pricing disadvantage to management of housing society.
Similarly, we included greenness in term of green spaces and areas AIC = 7509.90, BIC = 7499.98, R2 = 0.64. The value of \(\beta =8.993\)for that variable is significant at the 99% confidence interval (p < 0.01). Cho et al. (2008) identified that investors prefer urban residences that are near landscapes, green belts and rivers. It is generally presumed that as proximity to greenbelts decreases, ceteris paribus, the demand for residential properties near greenbelts will likewise increase. To the extent that demand increases, again, ceteris paribus, one would expect to observe an increase in the prices of residential properties near green belts.
Commercial and social amenities and preferences of residential housing societies tested by including in model, model credentials show that AIC = 8802.45, 7230.12 and BIC = 8708.50, 7210.09 and R2 = 0.66 & 65. The value of \(\beta =8.277 \text{a}\text{n}\text{d} 6.513\)for that variables are significant at the 99% confidence interval (p < 0.01). The explanatory power of both model is superior to that of Model-hedonic, as signified by a lower AIC and BIC and higher R2. Likewise, Previous studies found that the housing authority’s prestige is an important factor in pricing properties (Abidin et al., 2012; Hutchison & Disberry, 2015; Pugh, 2001). In order to test the importance of prestige of societies, we included prestige as explanatory variable and result shows that AIC = 7109.23, and BIC = 7089.29 and R2 = 0.64. The value of \(\beta =2.245\)for that variables are significant at the 99% confidence interval (p < 0.01). It shows that on the basis of environmental and socio-economic preferences, a housing society can build a positive identity before the customer which in return leads to better pricing and premium in market.
4.7 Estimation of Fair Price and Actual Price
We estimate the fair price, including a relatively extended set of factors that influence pricing, which allows us to measure price results. Then, we estimate the effect of potential factors include pollution, greenness, commercial, societal preferences and prestige of housing society that influence price and the results are reported in table (11) and predicted fair price and actual price as well as mean difference in table (12). The majority of price predictors are statistically significant in explaining the fair price in all the models. Similarly, pollution, greenness, commercial, societal preferences and prestige of housing society (\(\beta =\) -0.170, 9.629, 91.040, 3.831 and 3.091)\(\)have significant impact on the estimated price prediction as shown in table (11).
To measure price inefficiency, we generate fair price series and compare it with actual price as shown in figure (3–6) and subsequently take the difference between the actual and fair estimated prices. Table (12) indicates an average actual market price (\({\mu }_{1}=\) 27.445) million and the estimated fair price through SFA (\({\mu }_{2}\)= 27.726) million of the overall sample using hedonic model features, and the mean difference (\({\mu }_{1}-{\mu }_{2}\)= -0.281) million is insignificant. This deduces that on the basis of hedonic model features including property structure, size, location and location, housing societies cannot get premium or pricing advantage on each other. Customer fairly price the property based on the features of property.
Similarly, an average fair price is estimated using SFA (\({\mu }_{2}\)= 26.716) by including the pollution score in hedonic model and the mean difference with actual market price (\({\mu }_{1}-{\mu }_{2}\)= 0.729) that is significant at the 95% confidence level and comparison of both series is shown in figure (3).
Estimated fair price (\({\mu }_{2}\)= 30.286) million is calculated using SFA based on the inclusion of greenness score in hedonic model and the mean difference (\({\mu }_{1}-{\mu }_{2}\)= -2.841) million that is significant at the 99% confidence level comparison of both series is shown in figure (4).
Generally, Pakistan remains extremely vulnerable to the impacts of climate change. According to the Global Climate Risk Index 2020, Pakistan is ranked fifth in the list of countries most vulnerable to climate change. Between 1999 and 2018, the country witnessed 152 extreme weather events and suffered huge losses equaling USD 3.8 billion (UNDP, 2020). Furthermore, the index of dissatisfaction with time spent in the city for low polluted residential is lower compared to highly polluted areas, which indicates that people in high polluted areas do not enjoy living in it. Life in low polluted areas is far better, which is why more people like to pay more price for secure health environments.
Similarly, estimated fair price is calculated through SFA by including commercial, and community based amenities and preferences in hedonic model (\({\mu }_{2}\)= 30.128 and 30.152) million and mean difference of both features (\({\mu }_{1}-{\mu }_{2}\)= -2.683 and − 2.707) million that is significant at the 99% confidence level. Comparison of both series fair price and actual price based on commercial and social amenities are shown in figures (5 and 6).
There are several reasons behind the overpricing of the properties in High prestige housing societies, such as (a) basic amenities. These societies already have all the basic and civic amenities available for the residents to live comfortably here: 24 hours’ electricity backup for an uninterrupted power supply, water is available in these societies throughout the entire week, and the sewerage system is designed very meticulously. Furthermore, the availability of natural gas and cabling of these amenities is underground to provide a beautiful view to the residents. Second, recreational and commercial spheres of these societies are larger than least famous authorities such as the dancing fountain, the replica of Eiffel Tower, branches of fast foods, and commercial banks and increasing as more and more shops and outlets are being set up in these societies as compared to them. Third, many new shopping malls and galleries will be part of High prestige housing societies, which will increase the prices of nearby areas. These will also provide new and better commercial facilities so that residents can live comfortably.
Last but not least, prestige of housing societies is included in hedonic model and calculated fair price series (\({\mu }_{2}\)= 28.719) million and the mean difference (\({\mu }_{1}-{\mu }_{2}\)= -1.274) million that is significant at the 99% confidence level. Owing to the high demand for residential property by owners/occupiers as well as investors, developers of properties are continuously working to shift the supply curve to the right. Simultaneously, property prices in high prestige are rapidly escalating because of the right-ward shift of the demand curve.